Agentic RAG with LanceDB

This example demonstrates how to implement Agentic RAG using LanceDB vector database with OpenAI embeddings, enabling the agent to search and retrieve relevant information dynamically.

Code

1"""
21. Run: `pip install openai lancedb tantivy pypdf sqlalchemy kern-ai` to install the dependencies
32. Run: `python cookbook/rag/04_agentic_rag_lancedb.py` to run the agent
4"""
5
6from kern.agent import Agent
7from kern.knowledge.embedder.openai import OpenAIEmbedder
8from kern.knowledge.knowledge import Knowledge
9from kern.models.openai import OpenAIResponses
10from kern.vectordb.lancedb import LanceDb, SearchType
11
12knowledge = Knowledge(
13    # Use LanceDB as the vector database and store embeddings in the `recipes` table
14    vector_db=LanceDb(
15        table_name="recipes",
16        uri="tmp/lancedb",
17        search_type=SearchType.vector,
18        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
19    ),
20)
21
22knowledge.insert(
23    url="https://kern-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
24)
25
26agent = Agent(
27    model=OpenAIResponses(id="gpt-5.2"),
28    knowledge=knowledge,
29    # Add a tool to search the knowledge base which enables agentic RAG.
30    # This is enabled by default when `knowledge` is provided to the Agent.
31    search_knowledge=True,
32    markdown=True,
33)
34agent.print_response(
35    "How do I make chicken and galangal in coconut milk soup", stream=True
36)

Usage

Set up your virtual environment

1uv venv --python 3.12
2source .venv/bin/activate

1uv venv --python 3.12
2.venv\Scripts\activate

Install dependencies

1uv pip install -U kern-ai openai lancedb tantivy pypdf sqlalchemy

Export your OpenAI API key

1export OPENAI_API_KEY=your_openai_api_key_here

Run Agent

1python agentic_rag_lancedb.py